LANDED.

LANDED, founded in 2019 by Vivian Wang, is a piece of software for exactly that problem. It is built for high-volume, frontline hiring - restaurants, retail, hospitality - where an employer might post hundreds of roles and receive thousands of applications, and where the bottleneck is not judgment but volume. The company describes what it sells plainly enough: generative AI for frontline worker hiring.

The mechanics are the interesting part. A candidate applies, and instead of vanishing into a portal, they get a text message. An AI reads the resume. An AI asks the screening questions. An AI books the interview. The manager, who has an actual restaurant to run, shows up to a scheduled conversation with a pre-vetted person. The tedious middle - the part everyone hates - is handled by machines trained specifically on the data of more than two million hourly workers.

That last detail matters more than it sounds. There is a version of this business that is a thin wrapper around a general-purpose model, and there is a version built on proprietary data about how hourly hiring actually behaves - who shows up, who ghosts, what a good line-cook application looks like versus a good cashier application. LANDED is pitching the second version. Whether the moat holds is the open question of the category, but the bet is coherent.

The customers are recognizable. Five Guys. Cava. Panera. Chick-fil-A. Taco Bell. Torchy's Tacos. Salt & Straw. Cooper's Hawk. DIG. Hopdoddy. Erewhon. These are not companies with a hiring problem in the abstract; they are companies for whom hiring is a permanent, high-frequency operational cost, and for whom shaving days off time-to-hire is a real number on a real P&L.